Background
The client is a leading credit card provider headquartered in the United States. Established over 150 years ago, the client has over 100 million active cards and currently features amongst the top 100 companies in the ‘Fortune 500’ list.
The client was facing issues of low collections from defaulting credit card accounts and was looking to partner with an analytics expert for a reliable and effective solution. After evaluating several analytics firms, they reached out to Firstsource to improve collections in the top decile by improving the accuracy of propensity-to-pay predictions.
Business challenge
The client used a very basic prioritization model to identify accounts with the highest propensity-to-pay and was in urgent need of a more robust system.
- Propensity-to-pay prediction in the top decile was just a little over 78%
- The client was able to collect a meagre 2% of the total outstanding amount
- The limited information available for analysis included – outstanding amount, card holder’s state of residence and ‘probability-to-pay’ score accessed by the client. Vital details necessary for a thorough analysis, such as credit history and demographics couldn’t be shared on account of confidentiality restrictions.
Firstsource solution
Analysis framework
The Firstsource team analyzed a year of data and identified a number of segments within defined groups along with necessary variables and proxy variables such as ‘number of days since last payment’ and ‘CPS score’ for the new prioritization model. Using logistic regression on all groups, they developed a solution to perform clustering using the elbow method and identify deciles for the outstanding amount, number of cardholders that had paid and the amount paid. In the next phase, they successfully created a prioritization model that could assign propensity to pay for each account, leading to optimized sequencing.
